The Internet of Bio-Nano Things (IoBNT) is an emerging area of networking where seamless interconnection between biological and electrical nanotechnology-enabled devices is envisioned to enable disruptive applications. This paper focuses on characterizing the propagation of information (communication channel) through an interface between the biological and the electrical domains, whose components are commonly used as part of biosensing systems. On top of its sensing capabilities, this interface, based on electrochemical transduction, is here innovatively modeled and characterized in terms of communication performance, a key parameter in the context of IoBNT. In particular, the transduction at the basis of this channel is enabled by interactions with a circuit electrode of reduction-oxidation (redox) chemical reactions. Redox reactions are among the building blocks of communication channels in biological cells and tissues. Based on the underlying physical and chemical processes, an analytical system model is detailed for the channel. Based on the latter, an experimentally validated computational model is formulated to enable the empirical estimation of its performance through simulation data. The estimation of Signal-to-Noise Ratio (SNR), Limit of Detection (LoD), and Limit of Quantification (LoQ) are followed by frequency-based analysis of the channel and the estimation of an upper-bound to its information capacity. Design rules based on a trade-off between the dimension of the transducer and its communication performance are derived to aid in the integration of this communication channel into future IoBNT devices.

Information theory has been successfully applied to biology with interesting results and applications, ranging from scientific discovery, to system modeling, and engineering. Novel concepts such as semantic and useful information have been proposed to address the peculiarity of biological systems in contrast to Shannon's classical theory. In this paper, the concept of subjective information, previously observed as an emergent property in a simulated biological system with determinate characteristics, is further explored through the proposal of a novel metric for its quantification. This measure is based on a biological system's ability to dynamically sense and react to environmental signals to achieve a goal. The novel metric is validated through the simulation of a computational model that enables its correlation with different strategies for information acquisition from the environment and processing. The obtained results indicate that the proposed measure of subjective information is reliable in quantifying the effectiveness of a biological system's strategy in using information from the environment for its growth and survival.

The COVID-19 pandemic has resulted in one of the major challenges for humanity in the 21st century. The impact of these challenges has led to a tremendous loss of life, impact on long-term health, well-being as well as personal psychology, and negative societal changes and not to mention its impact on the global economy. Since this is a health issue, similar to other forms of diseases and pandemics, society has largely relied on the fields of medical, virology, immunology, biotechnology, and pharmaceutical science to develop novel therapeutic solutions for treatments. This has resulted in vaccines that have been rolled out to elevate immunity levels that will hopefully allow the majority of the population to reach herd immunity. However, given the technological advancements that we have reached in the 21st century, questions have also risen as to how other disciplines can play a role in solving and obtaining new knowledge of communicable disease pandemics.

The optimization of information transfer through molecule diffusion and chemical reactions is one of the leading research directions in Molecular Communication (MC) theory. The highly nonlinear nature of the processes underlying these channels poses challenges in adopting analytical approaches for their information-theoretic modeling and analysis. In this paper, a novel iterative methodology is proposed to numerically estimate achievable information rates. Based on the Nelder-Mead optimization, this methodology does not necessitate analytical for-mulations of MC components and their stochastic behavior, and, when applied to well-known scenarios, it demonstrates consistent results with theoretical bounds and superior performance to prior literature. A numerical example that abstracts communications between genetically engineered cells via simulation is presented and discussed in light of possible future applications to support the design and engineering of realistic MC systems.

MOTIVATION: Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic modifications and genetic variations. Computational modeling, as an essential research method, has generated promising testable quantitative models that represent complex interplay among different gene regulatory mechanisms based on these data in many biological systems. However, given the dynamic changes of interactome in chaotic systems such as cancers, and the dramatic growth of heterogeneous data on this topic, such promise has encountered unprecedented challenges in terms of model complexity and scalability. In this study, we introduce a new integrative machine learning approach that can infer multifaceted gene regulations in cancers with a particular focus on microRNA regulation. In addition to new strategies for data integration and graphical model fusion, a supervised deep learning model was integrated to identify conditional microRNA-mRNA interactions across different cancer stages. RESULTS: In a case study of human breast cancer, we have identified distinct gene regulatory networks associated with four progressive stages. The subsequent functional analysis focusing on microRNA-mediated dysregulation across stages has revealed significant changes in major cancer hallmarks, as well as novel pathological signaling and metabolic processes, which shed light on microRNAs' regulatory roles in breast cancer progression. We believe this integrative model can be a robust and effective discovery tool to understand key regulatory characteristics in complex biological systems. AVAILABILITY: http://sbbi-panda.unl.edu/pin/.

For more than a decade, Molecular Communication (MC), inspired by the natural way biological cells communicate, has been studied in engineering as the key paradigm for realizing computing and information systems increasingly integrated with biology. Broad application scenarios range from medical diagnostics and treatment, to biocompatible device and systems engineering. This paper focuses on a recently proposed technology able to transduce information from the MC domain to the electrical domain of classical circuits and systems. In particular, this is based on redox reactions, i. e., chemical processes where molecules exchange electrons, which play an important role in living systems. Based on these processes and a proof-of-concept prototype realized by our research collaborators, this paper addresses the computational modeling necessary to derive the design principles of such system. In particular, a stochastic simulation framework is developed to reproduce the transduction of information signals from the MC (input molecule concentration) to the electrical domain (output current). Numerical results from this framework, realized in MATLAB, are presented to evaluate the Signal-to-Noise Ratio (SNR), the Limit of Detection (LOD), and the Limit of Quantification (LOQ) of this transduction process as a function of the input concentration value. While at its preliminary stage, this framework has the potential to be foundational towards the engineering of a complete redox-based bio-hybrid electronics.

The design of communication systems capable of processing and exchanging information through molecules and chemical processes is a rapidly growing interdisciplinary field, which holds the promise to revolutionize how we realize computing and communication devices. While molecular communication (MC) theory has had major developments in recent years, more practical aspects in designing components capable of MC functionalities remain less explored. This paper designs chemical reactions-based microfluidic devices to realize binary concentration shift keying (BCSK) modulation and demodulation functionalities. Considering existing MC literature on information transmission via molecular pulse modulation, we propose a microfluidic MC transmitter design, which is capable of generating continuously predefined pulse-shaped molecular concentrations upon rectangular triggering signals to achieve the modulation function. We further design a microfluidic MC receiver capable of demodulating a received signal to a rectangular output signal using a thresholding reaction and an amplifying reaction. Our chemical reactions-based microfluidic molecular communication system is reproducible and its parameters can be optimized. More importantly, it overcomes the slow-speed, unreliability, and non-scalability of biological processes in cells. To reveal design insights, we also derive the theoretical signal responses for our designed microfluidic transmitter and receiver, which further facilitate the transmitter design optimization. Our theoretical results are validated via simulations performed through the COMSOL Multiphysics finite element solver. We demonstrate the predefined nature of the generated pulse and the demodulated rectangular signal together with their dependence on design parameters.

The birth of wireless communication systems nearly 1 century ago has transformed and redefined the way humans communicate and interact. This transformation, which has opened boundaries between societies and eliminated cultural barriers, has evolved from the original paradigm of wireless electromagnetic communication systems. This paradigm has experienced numerous evolutionary developments that have not only brought along seamless connectivity for human interactions but also communication between machines and devices.

The papers in this special issue aims at capturing the most relevant theoretical foundations of molecular communications and networking (MCN) established so far, while giving a timely perspective on some emerging technological directions for engineering practical MCN applications. We believe these special issue papers can serve as an essential but complete handbook to approach MC as a research topic, as well as to start contributing to its timely and much needed technological evolution from theoretical exploration to practical implementation.

Over the past 10 years, molecular communication (MC) has established itself as a key transformative paradigm in communication theory. Inspired by chemical communications in biological systems, the focus of this discipline is on the modeling, characterization, and engineering of information transmission through molecule exchange, with immediate applications in biotechnology, medicine, ecology, and defense, among others. Despite a plethora of diverse contributions, which has been published on the subject by the research community, a general framework to study the performance of MC systems is currently missing. This paper aims at filling this gap by providing an analysis of the physical processes underlying MC, along with their information-theoretic underpinnings. In particular, a mathematical framework is proposed to define the main functional blocks in MC, supported by general models from chemical kinetics and statistical mechanics. In this framework, the Langevin equation is utilized as a unifying modeling tool for molecule propagation in MC systems, and as the core of a methodology to determine the information capacity. Diverse MC systems are classified on the basis of the processes underlying molecule propagation, and their contribution in the Langevin equation. The classifications and the systems under each category are as follows: random walk (calcium signaling, neuron communication, and bacterial quorum sensing), drifted random walk (cardiovascular system, microfluidic systems, and pheromone communication), and active transport (molecular motors and bacterial chemotaxis). For each of these categories, a general information capacity expression is derived under simplifying assumptions and subsequently discussed in light of the specific functional blocks of more complex MC systems. Finally, in light of the proposed framework, a roadmap is envisioned for the future of MC as a discipline.

The birth of wireless communication systems nearly a century ago has transformed and redefined the way humans communicate and interact. This transformation has evolved over many years and has brought along not only seamless connectivity for human interactions but also communication between machines and devices. While these communication systems are manmade artifacts, the research community has more recently turned its attention to other communication strategies that have spontaneously evolved in nature.

Biological cells naturally exchange information for adapting to the environment, or even influencing other cells. One of the latest frontiers of synthetic biology stands in engineering cells to harness these natural communication processes for tissue engineering and cancer treatment, amongst others. Although experimental success has been achieved in this direction, approaches to characterize these systems in terms of communication performance and their dependence on design parameters are currently limited. In contrast to more classical communication systems, information in biological cells is propagated through molecules and biochemical reactions, which in general result in nonlinear input-output behaviors with system-evolution-dependent stochastic effects that are not amenable to analytical closed-form characterization. In this paper, a computational approach is proposed to characterize the information exchange in these systems, based on stochastic simulation of biochemical reactions and the estimation of information-theoretic parameters from sample distributions. In particular, this approach focuses on engineered cell-to-cell communications with a single transmitter and receiver, and it is applied to characterize the performance of a realistic system. Numerical results confirm the feasibility of this approach to be at the basis of future forward engineering practices for these communication systems.

The outbreak of the Ebola virus in recent years has resulted in numerous research initiatives to seek new solutions to contain the virus. A number of approaches that have been investigated include new vaccines to boost the immune system. An alternative post-exposure treatment is presented in this paper. The proposed approach for clearing the Ebola virus can be developed through a microfluidic attenuator, which contains the engineered bacteria that traps Ebola flowing through the blood onto its membrane. The paper presents the analysis of the chemical binding force between the virus and a genetically engineered bacterium considering the opposing forces acting on the attachment point, including hydrodynamic tension and drag force. To test the efficacy of the technique, simulations of bacterial motility within a confined area to trap the virus were performed. More than 60 percent of the displaced virus could be collected within 15 minutes. While the proposed approach currently focuses on in vitro environments for trapping the virus, the system can be further developed into a future treatment system whereby blood can be cycled out of the body into a microfluidic device that contains the engineered bacteria to trap viruses.

Synthetic biology, through genetic circuit engineering in biological cells, is paving the way toward the realization of programmable man-made living devices, able to naturally operate within normally less accessible domains, i.e., the biological and the nanoscale. The control of the information processing and exchange between these engineered-cell devices, based on molecules and biochemical reactions, i.e., molecular communication (MC), will be enabling technologies for the emerging paradigm of the Internet of Bio-Nano Things, with applications ranging from tissue engineering to bioremediation. In this paper, the design of genetic circuits to enable MC links between engineered cells is proposed by stemming from techniques for information coding and inspired by recent studies favoring the efficiency of analog computation over digital in biological cells. In particular, the design of a joint encoder-modulator for the transmission of binary-modulated molecule concentration is coupled with a decoder that computes the a-posteriori log-likelihood ratio of the information bits from the propagated concentration. These functionalities are implemented entirely in the biochemical domain through activation and repression of genes, and biochemical reactions, rather than classical electrical circuits. Biochemical simulations are used to evaluate the proposed design against a theoretical encoder/decoder implementation taking into account impairments introduced by diffusion noise.

In this paper, we present the design and implementation of a stand-alone tool for metabolic simulation and its deployment on the Open Science Grid (OSG). The case study model of glucose-insulin metabolism aims at the development of a real-time monitoring system that can assist patients with diabetes to handle their blood glucose profile and maintain healthy diet habit. This system is able to integrate custom-built SBML models along with users' food intake information and produces the estimation of ATP, Glucose, and Insulin for the given duration using numerical analysis and simulation. The tool has also been generalized to take into consideration of temporal genomic information and be flexible for simulation of any given biochemical models. After implementation on OSG, the results have demonstrated the effectiveness of numerical optimization for model selection and the feasibility of the proposed tool for the given metabolic simulation. The ATP-glucose and glucose-insulin correlations revealed by this tool can be promising for a variety of different application cases.

The design of communication systems capable of processing and exchanging information through molecules and chemical processes is a rapidly growing interdisciplinary field, which holds the promise to revolutionize how we realize computing and communication devices. While molecular communication (MC) theory has had major developments in recent years, more practical aspects in the design and prototyping of components capable of MC functionalities remain less explored. In this paper, motivated by a bulk of MC literature on information transmission via molecular pulse modulation, the design of a pulse generator is proposed as an MC component able to output a predefined pulse-shaped molecular concentration upon a triggering input. The chemical processes at the basis of this pulse generator are inspired by how cells generate pulse-shaped molecular signals in biology. At the same time, the slow-speed, unreliability, and non-scalability of these processes in cells are overcome with a microfluidic-based implementation based on standard reproducible components with well-defined design parameters. Mathematical models are presented to demonstrate the analytical tractability of each component, and are validated against a numerical finite element simulation. Finally, the complete pulse generator design is implemented and simulated in a standard engineering software framework, where the predefined nature of the output pulse shape is demonstrated together with its dependence on practical design parameters.

Regarded as one of the future enabling technologies of the Internet of Things at the biological and nanoscale domains, Molecular Communication (MC) promises to enable applications in healthcare, environmental protection, and bioremediation, amongst others. Since MC is directly inspired by communication processes in biological cells, the engineering of biological circuits through cells' genetic code manipulation, which enables access to the cells' information processing abilities, is a candidate technology for the future realization of MC components. In this paper, inspired by previous research on channel coding schemes for MC and biological circuits for cell communications, a joint encoder and modulator design is proposed for the transmission of cellular information through signaling molecules. In particular, the information encoding and modulation are based on a binary parity check scheme, and they are implemented by interconnecting biological circuit components based on gene expression and mass action reactions. Each component is mathematically modeled and tuned according to the desired output. The implementation of the biological circuit in a simulation environment is then presented along with the corresponding numerical results, which validate the proposed design by showing agreement with an ideal encoding and modulator scheme.

Molecular Communication (MC) is an enabling paradigm for the interconnection of future devices and networks in the biological environment, with applications ranging from bio-medicine to environmental monitoring and control. The engineering of biological circuits, which allows to manipulate the molecular information processing abilities of biological cells, is a candidate technology for the realization of MC-enabled devices. In this paper, inspired by recent studies favoring the efficiency of analog computation over digital in biological cells, an analog decoder design is proposed based on biological circuit components. In particular, this decoder computes the a-posteriori log-likelihood ratio of parity-check-encoded bits from a binary-modulated concentration of molecules. The proposed design implements the required L-value and the box-plus operations entirely in the biochemical domain by using activation and repression of gene expression, and reactions of molecular species. Each component of the circuit is designed and tuned in this paper by comparing the resulting functionality with that of the corresponding analytical expression. Despite evident differences with classical electronics, biochemical simulation data of the resulting biological circuit demonstrate very close performance in terms of Mean Squared Error (MSE) and Bit Error Rate (BER), and validate the proposed approach for the future realization of MC components.

Intra-body communication networks, where natural biological processes support the realization of links for the transmission, propagation, and reception of information, are at the cutting edge research for the pervasive interconnection of future wearable and implantable devices. In particular, the study of neurons as means to propagate information between these devices is encouraged by their ubiquitous distribution within the body and the existence of well-established techniques for their electrical interfacing. In this paper, a communication system is proposed based on the so-called subthreshold electrical stimulation of a neuron, and the propagation of this stimulation along the neuron length. This stimulation technique does not result in the generation of electrochemical spikes (action potentials), naturally carrying information within the nervous system, thus reducing the interference with the normal body functionalities. The use of subthreshold stimulation allows the analytical formulation of a linear channel model for the proposed communication system by stemming from the quasi-active model of the neuron's membrane from the neurophysiology literature. Numerical results from the developed analytical models are compared to simulation results obtained through the widely-used NEURON software.

The six papers in this special section focus on the Internet of nanotechnology things. While researchers are currently investigating these challenges to develop fully functional nano communication systems, a question remains as to whether they can represent an extended communication network that is part of the broader Internet. These papers address new solutions for the Internet of Nano Things. The Internet of Things paradigm has transformed the way we operate our personal and professional lives, it is driving our economy and will continue to enable many new opportunities in broad research areas. As this pervasive and ubiquitous interconnection of our everyday life appliances continues into the future, new types of devices enabled by nano and biotechnology promise to push engineering to previously unexplored application domains, where the exchange of information and access from/to the broader Internet for their monitoring and control are even more essential. The research on nanoscale communication and networks aims to develop systems for interconnecting these novel devices at the nanoscale, i.e., the Internet of Nano Things.

Molecular Communication (MC) is a nanoscale interaction paradigm inspired by the natural ability of cells in biology to communicate through the processing, exchange, and transduction of information by biochemical reactions of molecules. The design and modeling of MC systems is the first step towards future applications based on the engineering of communication systems in biology. In this paper, a bandpass filter based on a specific type of biochemical reactions in cell communication, namely, signaling kinase cascade, is proposed for an MC receiver in a diffusion-based MC scenario. In particular, under the commonly accepted weak activation assumption, these biochemical reactions can be analytically modeled through linear systems theory. The characterization of the proposed filter, and the corresponding numerical results, demonstrate its passband properties, and its suitability for extracting signals in different frequency bands coming from different molecular transmitters.

Novel emerging tools are allowing the manipulation and control of biological cells and their functions, e.g., sensing, actuation, and communication through biochemical stimuli. These tools have the potential to enable the implementation of manmade networks of biological computing devices, i.e., Internet of Bio-things. In this work, signal transduction pathways, i.e., cells' chemical reactions that process biochemical signals, are proposed for the design of analog linear filters to be utilized as components in the Internet of Bio-things. These filters, which exploit the crosstalk of signal transduction pathways to achieve the desired response, are here modeled and analyzed. The relations between filter properties and biochemical parameters are presented with the goal of designing a notch filter. A preliminary numerical example is also given as proof-of-concept.

Molecular nanonetworks stand at the intersection of nanotechnology, biotechnology, and network engineering. The research on molecular nanonetworks proposes the interconnection of nanomachines through molecule exchange. Amongst different solutions for the transport of molecules between nanomachines, the most general is based on free diffusion. The objective of this paper is to provide a statistical-physical modeling of the interference when multiple transmitting nanomachines emit molecules simultaneously. This modeling stems from the same assumptions used in interference study for radio communications, namely, a spatial Poisson distribution of transmitters having independent and identically distributed emissions, while the specific molecule emissions model is in agreement with a chemical description of the transmitters. As a result of the property of the received molecular signal of being a stationary Gaussian Process (GP), the statistical-physical modeling is operated on its Power Spectral Density (PSD), for which it is possible to obtain an analytical expression of the log-characteristic function. This expression leads to the estimation of the received PSD probability distribution, which provides a complete model of the interference in diffusion-based molecular nanonetworks. Numerical results in terms of received PSD probability distribution and probability of interference are presented to compare the proposed statistical-physical model with the outcomes of simulations.

Nanonetworks, the interconnection of nanosystems, are envisaged to greatly expand the applications of nanotechnology in the biomedical, environmental and industrial fields. However, it is still not clear how these nanosystems will communicate among them. This work considers a scenario of Diffusion-based Molecular Communication (DMC), a promising paradigm that has been recently proposed to implement nanonetworks. In a DMC network, transmitters encode information by the emission of molecules which diffuse throughout the medium, eventually reaching the receiver locations. In this scenario, a pulse-based modulation scheme is proposed and two techniques for the detection of the molecular pulses, namely, amplitude detection and energy detection, are compared. In order to evaluate the performance of DMC using both detection schemes, the most important communication metrics in each case are identified. Their analytical expressions are obtained and validated by simulation. Finally, the scalability of the obtained performance evaluation metrics in both detection techniques is compared in order to determine their suitability to particular DMC scenarios. Energy detection is found to be more suitable when the transmission distance constitutes a bottleneck in the performance of the network, whereas amplitude detection will allow achieving a higher transmission rate in the cases where the transmission distance is not a limitation. These results provide interesting insights which may serve designers as a guide to implement future DMC networks.

Molecular Communication (MC) is a communication paradigm based on the exchange of molecules. The implicit biocompatibility and nanoscale feasibility of MC make it a promising communication technology for nanonetworks. This paper provides a closed-form expression for the information capacity of an MC system based on the free diffusion of molecules, which is of primary importance to understand the performance of the MC paradigm. Unlike previous contributions, the provided capacity expression is independent from any coding scheme and takes into account the two main effects of the diffusion channel: the memory and the molecular noise. For this, the diffusion is decomposed into two processes, namely, the Fick's diffusion and the particle location displacement, which are analyzed as a cascade of two separate systems. The Fick's diffusion captures solely the channel memory, while the particle location displacement isolates the molecular noise. The MC capacity expression is obtained by combining the two systems as function of the diffusion coefficient, the temperature, the transmitter-receiver distance, the bandwidth of the transmitted signal, and the average transmitted power. Numerical results show that a few kilobits per second can be reached within a distance range of tenth of micrometer and for an average transmitted power around 1 pW.

Molecular Communication (MC) is a bio-inspired paradigm where information is exchanged by the release, the propagation and the reception of molecules. The objective of this paper is to analyze the effects of interference in the most general type of MC system, i.e., the diffusion of molecules in a fluidic medium. The study of the InterSymbol Interference (ISI) and Co-Channel Interference (CCI) is conducted through the analysis of the propagation of signals in a diffusion-based channel. An in-depth analysis of the attenuation and the dispersion of signals due to molecule diffusion allows to derive simple closed-form formulas for both ISI and CCI. In this paper, two different modulation schemes, namely, the baseband modulation and the diffusion wave modulation are considered for the release of molecules in the diffusion-based MC and are compared in terms of interference. It is determined that the diffusion wave modulation scheme shows lower interference values than the baseband modulation scheme. Moreover, it is revealed that the higher is the frequency of the modulating diffusion wave, the lower are the effects of the ISI and the CCI on the communication channel. The obtained analytical results are compared and validated by numerical simulation results.

Diffusion-based molecular communication is a promising bio-inspired paradigm to implement nanonetworks, i.e., the interconnection of nanomachines. The peculiarities of the physical channel in diffusion-based molecular communication require the development of novel models, architectures and protocols for this new scenario, which need to be validated by simulation. With this purpose, we present N3Sim, a simulation framework for diffusion-based molecular communication. N3Sim allows to simulate scenarios where transmitters encode the information by releasing molecules into the medium, thus varying their local concentration. N3Sim models the movement of these molecules according to Brownian dynamics, and it also takes into account their inertia and the interactions among them. Receivers decode the information by sensing the particle concentration in their neighborhood. The benefits of N3Sim are multiple: the validation of channel models for molecular communication and the evaluation of novel modulation schemes are just a few examples.

Molecular Communication (MC) is a promising bio-inspired paradigm in which molecules are transmitted, propagated and received between nanoscale machines. One of the main challenges is the theoretical study of the maximum achievable information rate (capacity). The objective of this paper is to provide a mathematical expression for the capacity in MC nanonetworks when the propagation of the information relies on the free diffusion of molecules. Solutions from statistical mechanics and thermodynamics are used to derive a closed-form expression for the capacity as function of physical parameters, such as the size of the system, the temperature and the number of molecules as well as of the bandwidth of the system and the transmitted power. An extremely high order of magnitude of the capacity numerical values demonstrates the enormous potential of the diffusion-based MC systems.

Molecular communication is a promising paradigm for nanoscale networks. The end-to-end (including the channel) models developed for classical wireless communication networks need to undergo a profound revision so that they can be applied for nanonetworks. Consequently, there is a need to develop new end-to-end (including the channel) models which can give new insights into the design of these nanoscale networks. The objective of this paper is to introduce a new physical end-to-end (including the channel) model for molecular communication. The new model is investigated by means of three modules, i.e., the transmitter, the signal propagation and the receiver. Each module is related to a specific process involving particle exchanges, namely, particle emission, particle diffusion and particle reception. The particle emission process involves the increase or decrease of the particle concentration rate in the environment according to a modulating input signal. The particle diffusion provides the propagation of particles from the transmitter to the receiver by means of the physics laws underlying particle diffusion in the space. The particle reception process is identified by the sensing of the particle concentration value at the receiver location. Numerical results are provided for three modules, as well as for the overall end-to-end model, in terms of normalized gain and delay as functions of the input frequency and of the transmission range.

In this paper we present a novel approach to 3-D human action classification based on the analysis of volumetric data obtained form the joint processing of video sequences acquired by a multiple-camera system. The use of volumetric data makes the system very robust and avoids problems related the typical human body self-occlusions and motion ambiguities, very common in an independent camera-by-camera analysis. A shape descriptor of a human body is obtained in order to capture only posture-dependent characteristics and its outputs at each time instant are collected together in action feature matrices. The use of dynamic time warping approach for action template matching accounts for possible temporal nonlinear distortions among different instances of the same gesture and allows gesture classification

We propose a human action clustering method based on a 3D representation of the body in terms of volumetric coordinates. Features representing body postures are extracted directly from 3D data, making the system inherently insensitive to viewpoint dependence, motion ambiguities and self-occlusions. An invariant shape descriptor of human body is obtained in order to capture only posture-dependent characteristics, despite possible differences in translation, orientation, scale and body size. Frame-by-frame descriptions, generated from a gesture sequence, are collected together in matrices. Clustering of action matrices is eventually performed, and through a dynamic time warping (while computing the distance metric), we gain independence from possible temporal nonlinear distortions among different instances of the same gesture.